---
title: "Reference: DatabaseConfig | RAG"
description: API reference for database-specific configuration types used with vector query tools in Mastra RAG systems.
---

import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";

# DatabaseConfig

The `DatabaseConfig` type allows you to specify database-specific configurations when using vector query tools. These configurations enable you to leverage unique features and optimizations offered by different vector stores.

## Type Definition

```typescript
export type DatabaseConfig = {
  pinecone?: PineconeConfig;
  pgvector?: PgVectorConfig;
  chroma?: ChromaConfig;
  [key: string]: any; // Extensible for future databases
};
```

## Database-Specific Types

### PineconeConfig

Configuration options specific to Pinecone vector store.

<PropertiesTable
  content={[
    {
      name: "namespace",
      type: "string",
      description:
        "Pinecone namespace for organizing and isolating vectors within the same index. Useful for multi-tenancy or environment separation.",
      isOptional: true,
    },
    {
      name: "sparseVector",
      type: "{ indices: number[]; values: number[]; }",
      description:
        "Sparse vector for hybrid search combining dense and sparse embeddings. Enables better search quality for keyword-based queries.  The indices and values arrays must be the same length.",
      isOptional: true,
      properties: [
        {
          type: "object",
          parameters: [
            {
              name: "indices",
              description: "Array of indices for sparse vector components",
              isOptional: false,
              type: "number[]",
            },
            {
              name: "values",
              description: "Array of values corresponding to the indices",
              isOptional: false,
              type: "number[]",
            },
          ],
        },
      ],
    },
  ]}
/>

**Use Cases:**

- Multi-tenant applications (separate namespaces per tenant)
- Environment isolation (dev/staging/prod namespaces)
- Hybrid search combining semantic and keyword matching

### PgVectorConfig

Configuration options specific to PostgreSQL with pgvector extension.

<PropertiesTable
  content={[
    {
      name: "minScore",
      type: "number",
      description:
        "Minimum similarity score threshold for results. Only vectors with similarity scores above this value will be returned.",
      isOptional: true,
    },
    {
      name: "ef",
      type: "number",
      description:
        "HNSW search parameter that controls the size of the dynamic candidate list during search. Higher values improve accuracy at the cost of speed. Typically set between topK and 200.",
      isOptional: true,
    },
    {
      name: "probes",
      type: "number",
      description:
        "IVFFlat probe parameter that specifies the number of index cells to visit during search. Higher values improve recall at the cost of speed.",
      isOptional: true,
    },
  ]}
/>

**Performance Guidelines:**

- **ef**: Start with 2-4x your topK value, increase for better accuracy
- **probes**: Start with 1-10, increase for better recall
- **minScore**: Use values between 0.5-0.9 depending on your quality requirements

**Use Cases:**

- Performance optimization for high-load scenarios
- Quality filtering to remove irrelevant results
- Fine-tuning search accuracy vs speed tradeoffs

### ChromaConfig

Configuration options specific to Chroma vector store.

<PropertiesTable
  content={[
    {
      name: "where",
      type: "Record<string, any>",
      description:
        "Metadata filtering conditions using MongoDB-style query syntax. Filters results based on metadata fields.",
      isOptional: true,
    },
    {
      name: "whereDocument",
      type: "Record<string, any>",
      description:
        "Document content filtering conditions. Allows filtering based on the actual document text content.",
      isOptional: true,
    },
  ]}
/>

**Filter Syntax Examples:**

```typescript
// Simple equality
where: { "category": "technical" }

// Operators
where: { "price": { "$gt": 100 } }

// Multiple conditions
where: {
  "category": "electronics",
  "inStock": true
}

// Document content filtering
whereDocument: { "$contains": "API documentation" }
```

**Use Cases:**

- Advanced metadata filtering
- Content-based document filtering
- Complex query combinations

## Usage Examples

<Tabs>
  <TabItem value="basic-usage" label="Basic Usage">
    ### Basic Database Configuration

    ```typescript
    import { createVectorQueryTool } from '@mastra/rag';

    const vectorTool = createVectorQueryTool({
      vectorStoreName: 'pinecone',
      indexName: 'documents',
      model: embedModel,
      databaseConfig: {
        pinecone: {
          namespace: 'production'
        }
      }
    });
    ```

  </TabItem>

  <TabItem value="runtime-override" label="Runtime Override">
    ### Runtime Configuration Override

    ```typescript
    import { RequestContext } from '@mastra/core/request-context';

    // Initial configuration
    const vectorTool = createVectorQueryTool({
      vectorStoreName: 'pinecone',
      indexName: 'documents',
      model: embedModel,
      databaseConfig: {
        pinecone: {
          namespace: 'development'
        }
      }
    });

    // Override at runtime
    const requestContext = new RequestContext();
    requestContext.set('databaseConfig', {
      pinecone: {
        namespace: 'production'
      }
    });

    await vectorTool.execute({
      context: { queryText: 'search query' },
      mastra,
      requestContext
    });
    ```

  </TabItem>

  <TabItem value="multi-database" label="Multi-Database">
    ### Multi-Database Configuration

    ```typescript
    const vectorTool = createVectorQueryTool({
      vectorStoreName: 'dynamic', // Will be determined at runtime
      indexName: 'documents',
      model: embedModel,
      databaseConfig: {
        pinecone: {
          namespace: 'default'
        },
        pgvector: {
          minScore: 0.8,
          ef: 150
        },
        chroma: {
          where: { 'type': 'documentation' }
        }
      }
    });
    ```

    :::note

**Multi-Database Support**: When you configure multiple databases, only the configuration matching the actual vector store being used will be applied.

:::

  </TabItem>

  <TabItem value="performance-tuning" label="Performance Tuning">
    ### Performance Tuning

    ```typescript
    // High accuracy configuration
    const highAccuracyTool = createVectorQueryTool({
      vectorStoreName: 'postgres',
      indexName: 'embeddings',
      model: embedModel,
      databaseConfig: {
        pgvector: {
          ef: 400,        // High accuracy
          probes: 20,     // High recall
          minScore: 0.85  // High quality threshold
        }
      }
    });

    // High speed configuration
    const highSpeedTool = createVectorQueryTool({
      vectorStoreName: 'postgres',
      indexName: 'embeddings',
      model: embedModel,
      databaseConfig: {
        pgvector: {
          ef: 50,         // Lower accuracy, faster
          probes: 3,      // Lower recall, faster
          minScore: 0.6   // Lower quality threshold
        }
      }
    });
    ```

  </TabItem>
</Tabs>

## Extensibility

The `DatabaseConfig` type is designed to be extensible. To add support for a new vector database:

```typescript
// 1. Define the configuration interface
export interface NewDatabaseConfig {
  customParam1?: string;
  customParam2?: number;
}

// 2. Extend DatabaseConfig type
export type DatabaseConfig = {
  pinecone?: PineconeConfig;
  pgvector?: PgVectorConfig;
  chroma?: ChromaConfig;
  newdatabase?: NewDatabaseConfig;
  [key: string]: any;
};

// 3. Use in vector query tool
const vectorTool = createVectorQueryTool({
  vectorStoreName: "newdatabase",
  indexName: "documents",
  model: embedModel,
  databaseConfig: {
    newdatabase: {
      customParam1: "value",
      customParam2: 42,
    },
  },
});
```

## Best Practices

1. **Environment Configuration**: Use different namespaces or configurations for different environments
2. **Performance Tuning**: Start with default values and adjust based on your specific needs
3. **Quality Filtering**: Use minScore to filter out low-quality results
4. **Runtime Flexibility**: Override configurations at runtime for dynamic scenarios
5. **Documentation**: Document your specific configuration choices for team members

## Migration Guide

Existing vector query tools continue to work without changes. To add database configurations:

```diff
const vectorTool = createVectorQueryTool({
  vectorStoreName: 'pinecone',
  indexName: 'documents',
  model: embedModel,
+ databaseConfig: {
+   pinecone: {
+     namespace: 'production'
+   }
+ }
});
```

## Related

- [createVectorQueryTool()](/reference/v1/tools/vector-query-tool)
- [Hybrid Vector Search](/examples/v1/rag/query/hybrid-vector-search)
- [Metadata Filters](/reference/v1/rag/metadata-filters)
